Wind effects on trees

Schindler, Dirk; Bauhus, Jürgen; Mayer, Helmut

doi:10.1007/s10342-011-0582-5

Cited by 51 publications

(37 citation statements)

References 62 publications

(61 reference statements)

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“…Wind, wind loading, gusts of winds, tree stature, tree species (and associated characteristics such as tree crown shape and density, root architecture and shape, and wood density), topography, soil, cumulative processes, etc. all play a role in the production of windthrows [11,[83][84][85][86][87][88] resulting in tree failure at a lower wind speed than expected [1,3]. Amazonia covers about 5.3 million km 2 , and has many environmental and functional gradients [89,90].…”

Section: Discussionmentioning

confidence: 99%

Windthrow Variability in Central Amazonia

et al. 2017

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Windthrows are a recurrent disturbance in Amazonia and are an important driver of forest dynamics and carbon storage. In this study, we present for the first time the seasonal and interannual variability of windthrows, focusing on Central Amazonia, and discuss the potential meteorological factors associated with this variability. Landsat images over the 1998-2010 time period were used to detect the occurrence of windthrows, which were identified based on their spectral characteristics and shape. Here, we found that windthrows occurred every year but were more frequent between September and February. Organized convective activity associated with multicell storms embedded in mesoscale convective systems, such as northerly squall lines (that move from northeast to southwest) and southerly squall lines (that move from southwest to northeast) can cause windthrows. We also found that southerly squall lines occurred more frequently than their previously reported~50 year interval. At the interannual scale, we did not find an association between El Niño-Southern Oscillation (ENSO) and windthrows.

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Section: Discussionmentioning

confidence: 99%

Windthrow Variability in Central Amazonia

et al. 2017

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“…The influence of the stand structure on the financial cost of forest damage caused by wind has been confirmed by numerous studies [52]. Another study [53] highlighted the fact that a stand's susceptibility to wind damage is not always heavily dependent on the species but is also often significantly associated with the depth of rooting and the soil type. Understanding the relationship between the root system and the soil is essential to an improvement in the modelling of root system immunity and in the subsequent uprooting of trees [54].…”

Section: Discussionmentioning

confidence: 71%

Application of GIS to Empirical Windthrow Risk Model in Mountain Forested Landscapes

Krejčí

Kolejka

Voženílek

et al. 2018

Forests

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Abstract:Norway spruce dominates mountain forests in Europe. Natural variations in the mountainous coniferous forests are strongly influenced by all the main components of forest and landscape dynamics: species diversity, the structure of forest stands, nutrient cycling, carbon storage, and other ecosystem services. This paper deals with an empirical windthrow risk model based on the integration of logistic regression into GIS to assess forest vulnerability to wind-disturbance in the mountain spruce forests of Šumava National Park (Czech Republic). It is an area where forest management has been the focus of international discussions by conservationists, forest managers, and stakeholders. The authors developed the empirical windthrow risk model, which involves designing an optimized data structure containing dependent and independent variables entering logistic regression. The results from the model, visualized in the form of map outputs, outline the probability of risk to forest stands from wind in the examined territory of the national park. Such an application of the empirical windthrow risk model could be used as a decision support tool for the mountain spruce forests in a study area. Future development of these models could be useful for other protected European mountain forests dominated by Norway spruce.

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“…This factor can vary significantly within a specific region (HALE et al, 2015) as produced maps. The topographic factors of these ecosystems are relatively complex as mountainous forest and could locally add to the general occurrence of high winds (SCHINDLER et al, 2012). The study of the windthrow has observed important wind effect variations caused by topography.…”

Section: Discussionmentioning

confidence: 99%

“…According to the census conducted in forest, range, and watershed management organization in Iran, it was found that timber volume more than 25% of the annual allowable cut was damaged by the wind in 2016. It means that in addition to disruption in cutting and transporting planning, revenues from non-salvaged timber decreased seriously (SCHINDLER et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Prediction Spatial Patterns of Windthrow Phenomenon in Deciduous Temperate Forests Using Logistic Regression and Random Forest

Shabani

Moslem

2017

CERNE

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Forest management needs to evaluate various hazards where may cause economic or other losses to forest owners. The aim of this study is to prepare windthrow hazard maps based on logistic regression and random forest models in Nowshahr Forests, Mazandaran Province, Iran. First of all, 200 windthrow locations were identified from extensive field surveys and some reports. Out these, 140 (70%) locations were randomly selected as training data and the remaining 60 (30%) cases were used for the validation goals. In the next step, 10 predictive variables such as slope degree, slope aspect, altitude, Topographic Position Index (TPI), Topographic Wetness Index (TWI), distance to roads and skid trails, wind effect, soil texture, forest type and stand density were extracted from the spatial database. Subsequently, windthrow hazard maps were produced using logistic regression and RF models, and the results were plotted in ArcGIS. Finally, the area under the curves (AUC) and kappa coefficient were made for performance purposes. The validation of results presented that the area under the curve and kappa have a more accuracy for the random forest (97.5%, and 95%, respectively) than logistic regression (96.667%, and 93.333%, respectively) model. Therefore, this technique has more potentiality to be applied in the evaluation of windthrow phenomenon in forest ecosystems. Additionally, both models indicate that the spatial distribution of windthrow incidence likelihood is highly variable in this region. In general, the mentioned findings can be applied for management of future windthrow in favor of economic benefits and environmental preservation.

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Wind effects on trees

Cited by 51 publications

References 62 publications

Windthrow Variability in Central Amazonia

Windthrow Variability in Central Amazonia

Application of GIS to Empirical Windthrow Risk Model in Mountain Forested Landscapes

Prediction Spatial Patterns of Windthrow Phenomenon in Deciduous Temperate Forests Using Logistic Regression and Random Forest

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